Advances in clinical research have yielded many evidence-based guidelines to help clinicians provide the right care for the right patient at the right time; however, studies suggest that nearly half the time, that care does not reach the patient. Clinical decision support (CDS) is an information technology-enabled method to identify missed opportunities and optimize care. This proposal seeks to systematically approach guidelines for cancer symptoms and translate these into computable representations that can be deployed using health IT systems to improve the outcomes of patients affected by cancer. To achieve this aim, first a team of clinical and technical experts will scan existing guidelines and decompose those selected into a set of coded clinical data elements (CIMI models) and work- and data-flows (knowledge artifacts) which describe how the recommended activities could be incorporated into care activities. Next, these clinical models will be automatically transformed into computable artifacts that are machine-readable (FHIR Profiles and Clinical Reasoning Artifacts). These artifacts can then be overlaid into existing IT systems or used as stand-alone applications by providers and patients to evaluate and treat cancer symptoms according to evidence-based guidance that maximizes quality of life and well-being.